{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VHvuI4dpcxYM"
      },
      "source": [
        "# Part 1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OHttLFAGRCX0"
      },
      "source": [
        "Note: Before running the code, make sure you upload the images you want to edit to Colab."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tEWYSTAiPUgA"
      },
      "outputs": [],
      "source": [
        "%pip install --quiet --upgrade diffusers transformers scipy ftfy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "krtAPL7tKA-J"
      },
      "outputs": [],
      "source": [
        "%pip install --quiet --upgrade accelerate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U7eXBBRZPqel"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from tqdm import tqdm\n",
        "from PIL import Image\n",
        "\n",
        "import torch\n",
        "from torch import autocast\n",
        "\n",
        "from transformers import CLIPTextModel, CLIPTokenizer\n",
        "from transformers import DPTForDepthEstimation, DPTFeatureExtractor\n",
        "\n",
        "from diffusers import AutoencoderKL, UNet2DConditionModel\n",
        "from diffusers.schedulers.scheduling_pndm import PNDMScheduler"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0B1NCql5jkmo"
      },
      "source": [
        "## Model definition"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fK0Cu4xnPe4E"
      },
      "outputs": [],
      "source": [
        "class DiffusionPipeline:\n",
        "\n",
        "    def __init__(self, \n",
        "                 vae, \n",
        "                 tokenizer, \n",
        "                 text_encoder, \n",
        "                 unet, \n",
        "                 scheduler):\n",
        "        \n",
        "        self.vae = vae\n",
        "        self.tokenizer = tokenizer\n",
        "        self.text_encoder = text_encoder\n",
        "        self.unet = unet\n",
        "        self.scheduler = scheduler\n",
        "        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "    \n",
        "    \n",
        "    def get_text_embeds(self, text):\n",
        "        # tokenize the text\n",
        "        text_input = self.tokenizer(text, \n",
        "                                    padding='max_length', \n",
        "                                    max_length=tokenizer.model_max_length, \n",
        "                                    truncation=True, \n",
        "                                    return_tensors='pt')\n",
        "        # embed the text\n",
        "        with torch.no_grad():\n",
        "            text_embeds = self.text_encoder(text_input.input_ids.to(self.device))[0]\n",
        "        return text_embeds\n",
        "\n",
        "\n",
        "    def get_prompt_embeds(self, prompt):\n",
        "        if isinstance(prompt, str):\n",
        "            prompt = [prompt]\n",
        "        # get conditional prompt embeddings\n",
        "        cond_embeds = self.get_text_embeds(prompt)\n",
        "        # get unconditional prompt embeddings\n",
        "        uncond_embeds = self.get_text_embeds([''] * len(prompt))\n",
        "        # concatenate the above 2 embeds\n",
        "        prompt_embeds = torch.cat([uncond_embeds, cond_embeds])\n",
        "        return prompt_embeds\n",
        "\n",
        "\n",
        "\n",
        "    def decode_img_latents(self, img_latents):\n",
        "        img_latents = 1 / self.vae.config.scaling_factor * img_latents\n",
        "        with torch.no_grad():\n",
        "            img = self.vae.decode(img_latents).sample\n",
        "        \n",
        "        img = (img / 2 + 0.5).clamp(0, 1)\n",
        "        img = img.cpu().permute(0, 2, 3, 1).float().numpy()\n",
        "        return img\n",
        "\n",
        "\n",
        "\n",
        "    def transform_img(self, img):\n",
        "        # scale images to the range [0, 255] and convert to int\n",
        "        img = (img * 255).round().astype('uint8')\n",
        "        # convert to PIL Image objects\n",
        "        img = [Image.fromarray(i) for i in img]\n",
        "        return img\n",
        "\n",
        "\n",
        "    def encode_img_latents(self, img, latent_timestep):\n",
        "        if not isinstance(img, list):\n",
        "            img = [img]\n",
        "        \n",
        "        img = np.stack([np.array(i) for i in img], axis=0)\n",
        "        # scale images to the range [-1, 1]\n",
        "        img = 2 * ((img / 255.0) - 0.5)\n",
        "        img = torch.from_numpy(img).float().permute(0, 3, 1, 2)\n",
        "        img = img.to(self.device)\n",
        "\n",
        "        # encode images\n",
        "        img_latents_dist = self.vae.encode(img)\n",
        "        img_latents = img_latents_dist.latent_dist.sample()\n",
        "        \n",
        "        # scale images\n",
        "        img_latents = self.vae.config.scaling_factor * img_latents\n",
        "        \n",
        "        # add noise to the latents\n",
        "        noise = torch.randn(img_latents.shape).to(self.device)\n",
        "        img_latents = self.scheduler.add_noise(img_latents, noise, latent_timestep)\n",
        "\n",
        "        return img_latents\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2uME1Fs7Vq8y"
      },
      "outputs": [],
      "source": [
        "class Depth2ImgPipeline(DiffusionPipeline):\n",
        "    def __init__(self, \n",
        "                 vae, \n",
        "                 tokenizer, \n",
        "                 text_encoder, \n",
        "                 unet, \n",
        "                 scheduler, \n",
        "                 depth_feature_extractor, \n",
        "                 depth_estimator):\n",
        "        \n",
        "        super().__init__(vae, tokenizer, text_encoder, unet, scheduler)\n",
        "\n",
        "        self.depth_feature_extractor = depth_feature_extractor\n",
        "        self.depth_estimator = depth_estimator\n",
        "\n",
        "\n",
        "    def get_depth_mask(self, img):\n",
        "        if not isinstance(img, list):\n",
        "            img = [img]\n",
        "\n",
        "        width, height = img[0].size\n",
        "        \n",
        "        # pre-process the input image and get its pixel values\n",
        "        pixel_values = self.depth_feature_extractor(img, return_tensors=\"pt\").pixel_values\n",
        "\n",
        "        # use autocast for automatic mixed precision (AMP) inference\n",
        "        with autocast('cuda'):\n",
        "            depth_mask = self.depth_estimator(pixel_values).predicted_depth\n",
        "        \n",
        "        # get the depth mask\n",
        "        depth_mask = torch.nn.functional.interpolate(depth_mask.unsqueeze(1),\n",
        "                                                     size=(height//8, width//8),\n",
        "                                                     mode='bicubic',\n",
        "                                                     align_corners=False)\n",
        "        \n",
        "        # scale the mask to range [-1, 1]\n",
        "        depth_min = torch.amin(depth_mask, dim=[1, 2, 3], keepdim=True)\n",
        "        depth_max = torch.amax(depth_mask, dim=[1, 2, 3], keepdim=True)\n",
        "        depth_mask = 2.0 * (depth_mask - depth_min) / (depth_max - depth_min) - 1.0\n",
        "        depth_mask = depth_mask.to(self.device)\n",
        "\n",
        "        # replicate the mask for classifier free guidance \n",
        "        depth_mask = torch.cat([depth_mask] * 2)\n",
        "        return depth_mask\n",
        "\n",
        "\n",
        "\n",
        "    \n",
        "    def denoise_latents(self, \n",
        "                        img,\n",
        "                        prompt_embeds,\n",
        "                        depth_mask,\n",
        "                        strength,\n",
        "                        num_inference_steps=50,\n",
        "                        guidance_scale=7.5,\n",
        "                        height=512, width=512):\n",
        "        \n",
        "        # clip the value of strength to ensure strength lies in [0, 1]\n",
        "        strength = max(min(strength, 1), 0)\n",
        "\n",
        "        # compute timesteps\n",
        "        self.scheduler.set_timesteps(num_inference_steps)\n",
        "\n",
        "        init_timestep = int(num_inference_steps * strength)\n",
        "        t_start = num_inference_steps - init_timestep\n",
        "        \n",
        "        timesteps = self.scheduler.timesteps[t_start: ]\n",
        "        num_inference_steps = num_inference_steps - t_start\n",
        "\n",
        "        latent_timestep = timesteps[:1].repeat(1)\n",
        "\n",
        "        latents = self.encode_img_latents(img, latent_timestep)\n",
        "\n",
        "        # use autocast for automatic mixed precision (AMP) inference\n",
        "        with autocast('cuda'):\n",
        "            for i, t in tqdm(enumerate(timesteps)):\n",
        "                latent_model_input = torch.cat([latents] * 2)\n",
        "                latent_model_input = torch.cat([latent_model_input, depth_mask], dim=1)\n",
        "                \n",
        "                # predict noise residuals\n",
        "                with torch.no_grad():\n",
        "                    noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=prompt_embeds)['sample']\n",
        "\n",
        "                # separate predictions for unconditional and conditional outputs\n",
        "                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n",
        "                \n",
        "                # perform guidance\n",
        "                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n",
        "\n",
        "                # remove the noise from the current sample i.e. go from x_t to x_{t-1}\n",
        "                latents = self.scheduler.step(noise_pred, t, latents)['prev_sample']\n",
        "\n",
        "        return latents\n",
        "\n",
        "\n",
        "    def __call__(self, \n",
        "                 prompt, \n",
        "                 img, \n",
        "                 strength=0.8,\n",
        "                 num_inference_steps=50,\n",
        "                 guidance_scale=7.5,\n",
        "                 height=512, width=512):\n",
        "\n",
        "\n",
        "        prompt_embeds = self.get_prompt_embeds(prompt)\n",
        "\n",
        "        depth_mask = self.get_depth_mask(img)\n",
        "\n",
        "        latents = self.denoise_latents(img,\n",
        "                                       prompt_embeds,\n",
        "                                       depth_mask,\n",
        "                                       strength,\n",
        "                                       num_inference_steps,\n",
        "                                       guidance_scale,\n",
        "                                       height, width)\n",
        "\n",
        "        img = self.decode_img_latents(latents)\n",
        "\n",
        "        img = self.transform_img(img)\n",
        "        \n",
        "        return img\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IQ0YObQCjbUU"
      },
      "source": [
        "## Create instance of the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SjVpjMXOPe6h"
      },
      "outputs": [],
      "source": [
        "device = 'cuda'\n",
        "\n",
        "# Load autoencoder\n",
        "vae = AutoencoderKL.from_pretrained('stabilityai/stable-diffusion-2-depth', subfolder='vae').to(device)\n",
        "\n",
        "# Load tokenizer and the text encoder\n",
        "tokenizer = CLIPTokenizer.from_pretrained('stabilityai/stable-diffusion-2-depth', subfolder='tokenizer')\n",
        "text_encoder = CLIPTextModel.from_pretrained('stabilityai/stable-diffusion-2-depth', subfolder='text_encoder').to(device)\n",
        "\n",
        "# Load UNet model\n",
        "unet = UNet2DConditionModel.from_pretrained('stabilityai/stable-diffusion-2-depth', subfolder='unet').to(device)\n",
        "\n",
        "# Load scheduler\n",
        "scheduler = PNDMScheduler(beta_start=0.00085, \n",
        "                          beta_end=0.012, \n",
        "                          beta_schedule='scaled_linear', \n",
        "                          num_train_timesteps=1000)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H5FipQQOKbiS",
        "outputId": "ff6719d7-cc17-4e3b-ff60-9342fe911af1"
      },
      "outputs": [],
      "source": [
        "# Load DPT Depth Estimator\n",
        "depth_estimator = DPTForDepthEstimation.from_pretrained('stabilityai/stable-diffusion-2-depth', subfolder='depth_estimator')\n",
        "\n",
        "# Load DPT Feature Extractor\n",
        "depth_feature_extractor = DPTFeatureExtractor.from_pretrained('stabilityai/stable-diffusion-2-depth', subfolder='feature_extractor')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IgWXvP7TPe8p"
      },
      "outputs": [],
      "source": [
        "depth2img = Depth2ImgPipeline(vae, \n",
        "                              tokenizer, \n",
        "                              text_encoder, \n",
        "                              unet, \n",
        "                              scheduler,\n",
        "                              depth_feature_extractor,\n",
        "                              depth_estimator)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u3End173jf5D"
      },
      "source": [
        "## Examples"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "PvdI6VVnPfAE",
        "outputId": "6ddb97f9-ef83-4430-eecb-3f156fe0c3dd"
      },
      "outputs": [],
      "source": [
        "import urllib.parse as parse\n",
        "import os\n",
        "import requests\n",
        "\n",
        "# a function to determine whether a string is a URL or not\n",
        "def is_url(string):\n",
        "    try:\n",
        "        result = parse.urlparse(string)\n",
        "        return all([result.scheme, result.netloc, result.path])\n",
        "    except:\n",
        "        return False\n",
        "\n",
        "\n",
        "# a function to load an image\n",
        "def load_image(image_path):\n",
        "    if is_url(image_path):\n",
        "        return Image.open(requests.get(image_path, stream=True).raw)\n",
        "    elif os.path.exists(image_path):\n",
        "        return Image.open(image_path)\n",
        "\n",
        "\n",
        "url = \"http://images.cocodataset.org/val2017/000000039769.jpg\"\n",
        "img = load_image(url)\n",
        "img"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "id": "B1FRHDHtFZ2x",
        "outputId": "b10b39ee-4ba5-4769-920f-1ea71c5de282"
      },
      "outputs": [],
      "source": [
        "depth2img(\"two tigers\", img)[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "WMLYkwDoGKUk",
        "outputId": "8e5d015f-187c-49b1-f863-4fba0abd2f45"
      },
      "outputs": [],
      "source": [
        "img = load_image(\"image16.png\")\n",
        "img"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "id": "bjBvAVZVGKWr",
        "outputId": "ad13f40d-de3a-480c-843c-b4e481a8b7a2"
      },
      "outputs": [],
      "source": [
        "prompt = \"A boulder with gemstones falling down a hill\"\n",
        "depth2img(prompt, img)[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "6eTFNAewGKZJ",
        "outputId": "4f906958-c76a-4cfe-dc2a-ca60272d1d08"
      },
      "outputs": [],
      "source": [
        "img = load_image(\"image11.png\").resize((512, 512))\n",
        "img"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DlJv2-1eN0da",
        "outputId": "a879b4b6-9c03-47be-8834-494365cb58b3"
      },
      "outputs": [],
      "source": [
        "import gc\n",
        "import torch\n",
        "\n",
        "# Run this cell if you get OOM - Out of Memory - errors\n",
        "torch.cuda.empty_cache()\n",
        "gc.collect()\n",
        "torch.cuda.empty_cache()\n",
        "gc.collect()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AhvSACo4N6g2",
        "outputId": "6292f90c-aaf7-4c73-9384-d5e59bf59b5b"
      },
      "outputs": [],
      "source": [
        "# just to check GPU memory\n",
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "id": "Uy0ziM_xGKbP",
        "outputId": "aa03b031-d5f7-48fa-ad48-22e7edcee10a"
      },
      "outputs": [],
      "source": [
        "prompt = \"A futuristic city on the edge of space, a robotic bionic singularity portal, sci fi, utopian, tim hildebrandt, wayne barlowe, bruce pennington, donato giancola, larry elmore\"\n",
        "depth2img(prompt, img)[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RryrZSIucvee"
      },
      "source": [
        "# Part 2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "37BGMrgkbgOX"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import requests\n",
        "from PIL import Image\n",
        "from diffusers import StableDiffusionDepth2ImgPipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1jqWIRGBbgR8"
      },
      "outputs": [],
      "source": [
        "pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(\n",
        "    \"stabilityai/stable-diffusion-2-depth\",\n",
        "    torch_dtype=torch.float16,\n",
        ").to(\"cuda\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a11H5bbvfjs1"
      },
      "source": [
        "## Impact of negative prompt example"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "7_HoqLyqrlta",
        "outputId": "26b03e01-d07d-4a6f-e9f5-642ddbb299f2"
      },
      "outputs": [],
      "source": [
        "img = load_image(\"https://images.pexels.com/photos/406152/pexels-photo-406152.jpeg?auto=compress&cs=tinysrgb&w=600\")\n",
        "img"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409,
          "referenced_widgets": [
            "b168918d31b34653897402751e9bbecc",
            "ed9bd4313a1a477a820abec0b6f911f6",
            "889529d86c064f73844e8074765f2cf0",
            "95ebc05ee7f640af86a54aadcd40ddbe",
            "a654808c82cc4672a19728ac22f26e5e",
            "f228ea9baedf457985c032deab6b96df",
            "665e483725974558b1712ef1fa235a7d",
            "2540be75baeb49769f279ad95778a857",
            "b2a6584f8d6f4c2ab221f8f90a1a560a",
            "ed5435b53cb646d080918c6720e222bd",
            "cd0e13f6e6dc444c80f7464747846e21"
          ]
        },
        "id": "mBngqY_Q7zRb",
        "outputId": "8c7a6992-514c-4bbf-dcb3-4b6f5774d311"
      },
      "outputs": [],
      "source": [
        "prompt = \"A salad with tomatoes and guanas chips mixed with ketchup and mustard and bay leaf and guacamole and onions and ketchup and luscious patty with sesame seeds and cashews and onions and ketchup, ethereal,\"\n",
        "pipe(prompt=prompt, image=img, negative_prompt=None, strength=0.7).images[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409,
          "referenced_widgets": [
            "71f962cddf284a1fbc5f882dc61d673a",
            "2ad534b30374491ca6b99f035efa1004",
            "db69c9d0fd214da8b42b854d71b87f54",
            "6041b74d03bd48f0adea3662f0b9bc27",
            "8ec8c55bace044fd885ed6ada6c3cd23",
            "b3c65e0ad46a4fdcb639e3c98983f1b7",
            "7d12e7d3738f4094a2c1d7a56a03c928",
            "5e4d7e7e89cf4b8e9cab8d34d88d662e",
            "5066ac8781ec4f00b27ec6be27eb8506",
            "a3a361916b864a3cadb650a563842b16",
            "279e8805d1a8468496bf091b2854683e"
          ]
        },
        "id": "aj197V-_RYWl",
        "outputId": "038b263c-01d7-4e7d-f49b-5b26d3661152"
      },
      "outputs": [],
      "source": [
        "prompt = \"A salad with tomatoes and guanas chips mixed with ketchup and mustard and bay leaf and guacamole and onions and ketchup and luscious patty with sesame seeds and cashews and onions and ketchup, ethereal,\"\n",
        "n_prompt = \"ugly, deformed, not detailed, bad architectures, blurred, too much blurred, motion blur\"\n",
        "pipe(prompt=prompt, image=img, negative_prompt=n_prompt, strength=0.7).images[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 785
        },
        "id": "qnPTkMvOQeM0",
        "outputId": "ce7d32c7-fac1-4df7-b9e2-ea9cedd12b5a"
      },
      "outputs": [],
      "source": [
        "img = load_image(\"image15.png\")\n",
        "img"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817,
          "referenced_widgets": [
            "3f2e54c7185e4251962269369e30a9ec",
            "f037d2a0f68e4647ab8d1e3c278aac8f",
            "77bdcf219f2e4aa8ba1725329011f2bc",
            "bf5ffbefc35449659cc9aae956e9a292",
            "6186ca097ee64f289f1becdd180cf1aa",
            "f50555e1470b4a8e95ea430871af77cf",
            "9f6a32585a514034975b71b2954b639f",
            "41c7ad82fa3f4a24a13a09f384e8fd51",
            "3cd041ccf57c46ff91b714b7c572f581",
            "7d60b538a6e248369d2df053e9548a14",
            "a2bc87dd43e14ee0abba026d57cb349f"
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        },
        "id": "8thR1RQ08HPI",
        "outputId": "5b9c661a-e291-4d68-8fde-5bb6f081d8f9"
      },
      "outputs": [],
      "source": [
        "prompt = \"Last remaining old man on earth\"\n",
        "n_prompt = \"bad anatomy, ugly, wrinkles\"\n",
        "pipe(prompt=prompt, image=img, negative_prompt=n_prompt, strength=0.7).images[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_HH2z2bXfaYV"
      },
      "source": [
        "## Changing strength - Futuristic city example"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cNIgur9_91-8"
      },
      "outputs": [],
      "source": [
        "img = load_image(\"image11.png\")\n",
        "img"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817,
          "referenced_widgets": [
            "95b5dce9d5e140e78849c5aae374f27c",
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            "0f79744697af4a53835dee31a1b4f748",
            "7dd5be64da7a41f8875765186bdd0b10",
            "8dccd6692afc41caa28072f35667bfac",
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            "650ea058ba304da9b8febd6a72e10260",
            "f3d5fc14999740b2bb7e4c21d1413b37",
            "b274cfa1678e49938e163e2d17d7f179"
          ]
        },
        "id": "aQBJTWJG92Mr",
        "outputId": "d612403b-593c-4796-af26-bbd61e1c1ee6"
      },
      "outputs": [],
      "source": [
        "prompt = \"A futuristic city\"\n",
        "pipe(prompt=prompt, image=img, negative_prompt=None, strength=0.7).images[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817,
          "referenced_widgets": [
            "c33aa07ac5a741adbef140e57b9702e2",
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            "80e10875f83e4889ad1840255ef9c49d",
            "e7da8d3d63f740968550b2852880ccc1",
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          ]
        },
        "id": "bbgJ1lQw92PO",
        "outputId": "c35afeb3-e07f-406e-9088-2b032292e405"
      },
      "outputs": [],
      "source": [
        "prompt = \"Futuristic city, modern, highly detailed, aesthetic, octane render, 8K, UHD, photoshopped\"\n",
        "n_prompt = \"ugly, deformed, not detailed, bad architectures, blurred, too much blurred, motion blur\"\n",
        "pipe(prompt=prompt, image=img, negative_prompt=n_prompt, strength=0.7).images[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817,
          "referenced_widgets": [
            "cdcc3550ed8340ebb2e8f0f903500c9c",
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            "b86219c801bb4d2e8395ce17518fa432",
            "3ff6eda7d146478880b0d26670caa23c",
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            "2cc5cba3a49842fab50519d9968a4e53"
          ]
        },
        "id": "kjubi4RD92Sn",
        "outputId": "3717a51a-63b1-45a9-df8a-f096aa533ded"
      },
      "outputs": [],
      "source": [
        "pipe(prompt=prompt, image=img, negative_prompt=n_prompt, strength=0.1).images[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817,
          "referenced_widgets": [
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            "6c4f2c3e26614dce84bc1bf63b1e263f"
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        },
        "id": "fHqLC7NGALi9",
        "outputId": "96b0fda0-40a3-4d7a-f702-19c48230c2c9"
      },
      "outputs": [],
      "source": [
        "pipe(prompt=prompt, image=img, negative_prompt=n_prompt, strength=0.5).images[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817,
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          ]
        },
        "id": "dqMjMcJMARNr",
        "outputId": "b07157f3-ece3-428b-b595-53f2a4304898"
      },
      "outputs": [],
      "source": [
        "pipe(prompt=prompt, image=img, negative_prompt=n_prompt, strength=0.9).images[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817,
          "referenced_widgets": [
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            "708184f7d3b240a38f17656f8bbfd8ae",
            "a9651ab87eae4180a9729b7e28f11376",
            "061de01186f440bea0491134ff347c86",
            "15b2d929ce0a4c70a156302db442e1a9",
            "4c55892c8dd3450a9904bce9e9b80057",
            "02dae045c1e7433cbdd872206d35e2f4",
            "d75aad33f36b432c8175e076ce4cc99e",
            "a3073940c26743b79c7477cbc92cb8e0"
          ]
        },
        "id": "HRkexFAEALls",
        "outputId": "0905b122-6f11-41c2-8982-6167973e70fd"
      },
      "outputs": [],
      "source": [
        "pipe(prompt=prompt, image=img, negative_prompt=n_prompt, strength=1).images[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IXvOug64fM6H"
      },
      "source": [
        "## Article beginning examples"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 785
        },
        "id": "HhA7oF_OA6SY",
        "outputId": "66afe568-50ff-4bc1-fe07-ca1807fec2bc"
      },
      "outputs": [],
      "source": [
        "img = load_image(\"image12.png\")\n",
        "img"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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          "referenced_widgets": [
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        },
        "id": "T58XQSFpDEfZ",
        "outputId": "a6f4a271-6a91-4578-e7f0-89800ed684f8"
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      "outputs": [],
      "source": [
        "prompt = \"World war, aesthetic\"\n",
        "n_prompt = \"bad looking, deformed, wholesome\"\n",
        "pipe(prompt=prompt, image=img, negative_prompt=n_prompt, strength=0.9).images[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      "outputs": [],
      "source": [
        "img = load_image(\"image3.png\")"
      ]
    },
    {
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      "metadata": {
        "colab": {
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        "id": "td1Jm19GIMzA",
        "outputId": "15685051-9943-4f05-cd5b-71b7830870d1"
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      "outputs": [],
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        "prompt = \"Beautiful anime landscape\"\n",
        "n_prompt = \"bad, deformed, ugly\"\n",
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    {
      "cell_type": "code",
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